pyprobound.rounds.RhoGammaRound

class RhoGammaRound(aggregate, reference_round, train_depth=True, log_depth=0, library_concentration=-1, rho=0, gamma=-1, name='')

Bases: Round

Enrichment round with learned saturation parameters.

\[\frac{f_{i,r}}{f_{i,r-1}} = \frac{Z_{i,r}^\rho}{(1+Z_{i,r})^\gamma}\]

aggregate

The binding components driving enrichment.

Type:: Aggregate

reference_round

The previous round for cumulative enrichment.

Type:: BaseRound

log_depth

The sequencing depth \(\eta\) in log space.

Type:: Tensor

rho

The parameter \(\rho\).

Type:: Tensor

gamma

The parameter \(\gamma\).

Type:: Tensor

__init__(aggregate, reference_round, train_depth=True, log_depth=0, library_concentration=-1, rho=0, gamma=-1, name='')

Initializes the round.

Parameters:

aggregate (Aggregate) – The binding components driving enrichment.
reference_round (BaseRound | None) – The previous round used in cumulative enrichment.
train_depth (bool) – Whether to train the sequencing depth \(\eta\).
log_depth (float) – The initial value of log_depth.
library_concentration (float) – The total library concentration, used for estimating the free protein concentration.
rho (float) – The initial value of rho.
gamma (float) – The initial value of gamma.
name (str) – A string used to describe the round.

Methods

`cache`(fun)	Decorator for a function to cache its output.
`check_length_consistency`()	Checks that input lengths of Binding components are consistent.
`components`()	Iterator of child components.
`forward`(seqs)	Predicts the log relative count.
`freeze`()	Turns off gradient calculation for all parameters.
`from_binding`(binding, reference_round[, ...])	Creates a new instance from binding components and reference round.
`log_aggregate`(seqs)	Predicts the log aggregate \(\log Z_i\).
`log_cumulative_enrichment`(seqs)	Predicts the log cumulative enrichment.
`log_enrichment`(seqs)	Predicts the log enrichment ratio.
`max_embedding_size`()	The maximum number of bytes needed to encode a sequence.
`optim_procedure`([ancestry, current_order])	The sequential optimization procedure for all Binding components.
`reload`(checkpoint)	Loads the model from a checkpoint file.
`reload_from_state_dict`(state_dict)	Loads the model from a state dict.
`save`(checkpoint[, flank_lengths])	Saves the model to a file with "state_dict" and "metadata" fields.
`unfreeze`([parameter])	Turns on gradient calculation for the specified parameter.

Attributes

`library_concentration`	The total library concentration \([\text{library}]\).
`unfreezable`	alias of `Literal`['all', 'depth', 'rho']

Non-Inherited Members

unfreezable: alias of Literal[‘all’, ‘depth’, ‘rho’]

classmethod from_binding(binding, reference_round, train_depth=True, log_depth=0, train_concentration=False, target_concentration=1, library_concentration=-1, activity_heuristic=0.05, name='', rho=1, gamma=-1)

Creates a new instance from binding components and reference round.

Parameters:

binding (Iterable[Binding]) – The binding components driving enrichment.
reference_round (BaseRound | None) – The previous round used in cumulative enrichment.
train_depth (bool) – Whether to train the sequencing depth \(\eta\).
log_depth (float) – The initial value of log_depth.
train_concentration (bool) – Whether to train the protein concentration.
target_concentration (float) – Protein concentration, used for estimating the free protein concentration.
library_concentration (float) – Library concentration, used for estimating the free protein concentration.
activity_heuristic (float) – The fraction of the total aggregate that the contribution will be set to when it is first optimized.
name (str) – A string used to describe the round.
rho (float) – The initial value of rho.
gamma (float) – The initial value of gamma.

Return type:

Self

unfreeze(parameter='all')

Turns on gradient calculation for the specified parameter.

Parameters:: parameter (unfreezable) – Parameter to be unfrozen, defaults to all parameters.
Return type:: None

log_enrichment(seqs)

Predicts the log enrichment ratio.

\[\log \frac{f_{i,r}}{f_{i,r-1}}\]

Parameters:: seqs (Tensor) – A sequence tensor of shape \((\text{minibatch},\text{length})\) or \((\text{minibatch},\text{in_channels},\text{length})\).
Return type:: Tensor
Returns:: The log enrichment ratio tensor of shape \((\text{minibatch},)\).